Modern data centers often comprise thousands of hosts that operate collectively to service requests from even larger numbers of remote clients. During operation, components of these data centers can produce significant volumes of machine-generated data. The unstructured nature of much of this data has made it challenging to perform indexing and searching operations because of the difficulty of applying semantic meaning to unstructured data. As the number of hosts and clients associated with a data center continues to grow, processing large volumes of machine-generated data in an intelligent manner continues to be a priority.
Additionally, effectively presenting the results of such processing presents a separate challenge. Typically, queries are submitted, processed, and visualized individually in virtual dashboards or notebook environments, for instance. The display of subsequent queries and corresponding visualizations of search result information often replace the results of previous queries in their entireties, therefore requiring users to instantiate a new instance of the query application or display in order to view multiple queries and query results simultaneously. However, in many implementations, the results of the queries are current only at the time the query is processed, and updates to the underlying data set may not be reflected in these visualizations. Moreover, certain implementations provide the ability to submit queries that directly reference data from previously submitted queries. However, as often is the case when the data set is sufficiently large and continuously streaming, the data received from processing the earlier query may no longer be up to date by the time the more recent query is processed. Under these circumstances, visualization of the more recent query may be generated with inaccurate, incomplete, or obsolete data.